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Performance Evaluation of Orbital Angular Momentum Mode Multiplexing Systems Impaired by Phase Noise

2022· article· en· W4211208475 on OpenAlexaff
Peyman Neshaastegaran, Ming Jian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsMultiplexingAngular momentumPhysicsInterference (communication)Phase noiseNoise (video)Mode (computer interface)Orbital angular momentum multiplexingTopology (electrical circuits)Signal-to-noise ratio (imaging)Electronic engineeringOpticsComputer scienceTelecommunicationsMathematicsEngineeringTotal angular momentum quantum numberOrbital angular momentum of lightQuantum mechanicsCombinatorics

Abstract

fetched live from OpenAlex

In this paper, the performance of orbital angular momentum (OAM) mode multiplexing systems is investigated in the presence of oscillator phase noise (PN). First, the OAM spectrum is calculated for an OAM carrying beam that is generated by a uniform circular array (UCA) connected to an imperfect oscillator. It is shown that the OAM mode purity is insensitive to PN. Subsequently, the system model of the UCA-based OAM multiplexing in the presence of PN is presented. Using this model, the signal-to-interference-plus-noise ratio (SINR) and the sum-rate of the system are derived as a function of PN statistics. The significant reduction in the system throughput due to the PN confirms the necessity of an effective PN mitigation scheme. Hence, the system model after applying a generic pilot-based PN mitigation is also investigated, and the SINR and the sum-rate after PN compensation are analytically derived. Simulation results, generated for various PN models, reveal the existing design trade-offs between the pilot overhead and the sum-rate in these systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.279
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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